Reinforcement Learning

Salesforce Open-Sources ‘WarpDrive’, A Light Weight Reinforcement Learning (RL) Framework That Implements End-To-End Multi-Agent RL On A Single GPU

AI Paper Summary
When it comes to AI research and applications, multi-agent systems are a frontier. They have been used for engineering challenges such as self-driving cars,...

DeepMind Introduces XLand: An Open-Ended 3D Simulated Environment Space To Train and Evaluate Artificial Agents

AI Paper Summary
Deep reinforcement learning (deep RL) has seen promising advances in recent years and produced highly performant artificial agents across a wide range of training...

AI Research Team From Princeton, Berkeley and ETH Zurich Introduce ‘RLQP’ To Accelerate Quadratic Optimization With Deep Reinforcement Learning (RL)

AI Paper Summary
Quadratic programming (QPs) is widely used in various fields, including finance, robotics, operations research, and many others, for large-scale machine learning and embedded optimal...

Facebook AI Open-Sources ‘Droidlet’, A Platform For Building Robots With Natural Language Processing And Computer Vision To Understand The World Around Them

AI Paper Summary
Robots today have been programmed to vacuum the floor or perform a preset dance, but there is still much work to be done before...

Joanneum Research Institute Release Version 1.0.0 Of Robo-Gym, An Open Source Toolkit For Distributed Deep Reinforcement Learning On Real And Simulated Robots

AI Paper Summary
Deep Reinforcement Learning (DRL) has proven to be extremely effective when it comes to complex tasks in robotics. Most of the work done with...

Facebook AI Introduces Habitat 2.0: Next-Generation Simulation Platform Provides Faster Training For AI Agents With Tactile Perception

AI Paper Summary
Facebook recently announced Habitat 2.0, a next-generation simulation platform that lets AI researchers teach machines to navigate through photo-realistic 3D virtual environments and interact...

US Army Researchers Develop A New Framework For Collaborative Multi-Agent Reinforcement Learning Systems

AI Paper Summary
Centralized learning for multi-agent systems highly depends on information-sharing mechanisms. However, there have not been significant studies within the research community in this domain. Army...

AI Researchers Including Yoshua Bengio, Introduce A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning

AI Paper Summary
Human consciousness is an exceptional ability that enables us to generalize or adapt well to new situations and learn skills or new concepts efficiently....

Researchers from ETH Zurich Propose a Novel Robotic Systems Capable of Self-Improving Semantic Perception

AI Paper Summary
Mobile robots are generally deployed in highly unstructured environments. They need to not only understand the various aspects of their environment but should also...

Researchers from UC Berkeley and CMU Introduce a Task-Agnostic Reinforcement Learning (RL) Method to Auto-Tune Simulations to the Real World

AI Paper Summary
Applying Deep Learning techniques to complex control tasks depends on simulations before transferring models to the real world. However, there is a challenging “reality...

Researchers at ETH Zurich and UC Berkeley Propose Deep Reward Learning by Simulating The Past (Deep RLSP)

AI Paper Summary
In Reinforcement Learning (RL), the task specifications are usually handled by experts. It needs a lot of human interaction to Learn from demonstrations and...

Researchers From Microsoft and Princeton University Find Text-Based Agents can Achieve High Scores Even in The Complete Absence of Semantics

AI Paper Summary
Recently, Text-based games have become a popular testing method for developing and testing reinforcement learning (RL). It aims to build autonomous agents that can...
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